Pharmacovigilance (PV), also known as Drug Safety Surveillance, is the pharmacologic science relating to the collection, detection, assessment, monitoring, and prevention of adverse effects with pharmaceutical products. This is an important process that allows regulatory authorities to continue to assess benefits and risks throughout the life-cycle of a drug and potentially detect serious adverse events and identify new drug safety signals that were previously undetected by typical marketing authorization. The process generally involves medical information, which can be received from patients, healthcare providers, medical literature, physicians, pharmaceutical company's sales team, pharmacists, or the like. Information collected from different sources needs to be processed in a defined consistent way for electronic submission to the regulatory authorities like FDA (Food and Drug Authority), WHO (World Health Organization), MHRA (Medicines and Health Regulatory Agency), EMA (European Medicines Agency) and other local authorities. Apart from regulatory requirements, pharmaceutical companies need to engage in pharmacovigilance to serve public health, and to foster a sense of trust with patients who used the drug, and to proactively monitor drug effects to prevent product withdrawal from market due to safety issues.
Maintaining a robust pharmacovigilance system relies on consistent and accurate acquisition, integration and analysis of adverse event data. Without a strong foundation, important safety signals may not be fully identified and evaluated. Some studies estimate that as much as 30% of all drug reactions result from concomitant use and that an estimated 29.4% of elderly patients are on six or more drugs. Several published drug-safety papers have shown that adverse effects of drugs may be detected too late when millions of patients have already been exposed to them. For a long time, researchers have been seeking a real time, continuous and prospective approach that could integrate vast, dispersed and unstructured information and knowledge bases to obtain unambiguous drug reaction relationships to automate the narrative generation process. However, for a single patient, this may require processing numerous medical records, which can be a time consuming process requiring the expertise of medical professionals. The difficulty in maintaining vigilance over a drug's effect on patients is further compounded by the fact that the drug may be given to large patient populations, both during trial and once on the market. Furthermore, government regulatory agencies, such as the FDA, require prompt and detailed reporting of this information.
As mentioned above, analyzing possible drug safety incidents and generating narratives in the pharmacovigilance process have traditionally relied upon manual review of case reports from patients, consumers and healthcare professionals, which may involve literature searching, case screening, case processing, narrative generation, and medical review. However, due to the vast quantity and complexity of data to be analyzed and the need for ensuring timeliness, reduced costs, and consistency and quality of reporting, such methods are not well suited and are generally time consuming and expensive. For instance, case processing and narrative generation may take several hours, and the medical review process may be iterative in nature requiring multiple reviews and several data lookups to establish causality. It may also be the case that there are not enough trained medical personnel to perform the task. Automating this process is also difficult given the volume of medical records that need to be processed and the fact that such data is provided in disparate formats. Additionally, meaningful analysis of the data requires identification of complex relationships that may not be readily apparent, even to trained professionals.
While computer-based systems have been developed to tackle this problem, existing computer-based systems only perform natural language processing in a limited capacity, and such systems are simply unable to investigate the relationships between the drugs, diseases (manifested through their system organ classes) and reactions in a sufficiently robust and complex automated fashion. The pharmacovigilance system described below seeks to address the limitations of current computer-based systems.
In particular, traditional computer-based systems are limited in their ability to identify relationships, in part, because the architecture of these systems and processes fail to account for the underlying clinical knowledge databases being disparate in their structure and management. Despite the need for a collaborative knowledge framework to automate the pharmacovigilance process through semantic integration of these databases, there has not been a successful effort within the industry to establish a relationship between multiple databases to assist the pharmacovigilance process.
Furthermore, given the limitations in traditional computer-based systems, substantial manual effort is still needed after processing the clinical text, for example, requiring manual look-up and review of different databases and manual identification of medical causation from these distinct data sources. However, because medical reviewers are often times familiar with only a handful of data sources and are prone to human error, manual identification of relationships in the clinical text is not always accurate or complete.
Furthermore, most of the text analytics based work in the pharmacovigilance domain has been restricted to academic and research purposes and address only a few of the sub-processes involved in the complete process chain. Complete end-to-end processing of adverse event (AE) reaction reports are typically unsupported by such systems. For the reasons noted above, human intervention can result in inconsistent, inaccurate and incomplete report generation, which has presented a huge hurdle in gaining the trust of end users and regulatory authorities.
While typical machine learning processes applying hidden Markov models or conditional random field analysis may seem suited for addressing some of these difficulties, such solutions have proven inadequate. Such methods have failed in part due to the unavailability of suitable annotated data, or the time and monetary expense required in creating such data. Moreover, in analogous contexts, it has been shown that the performance of machine learning methods is sub-optimal.
Accordingly, there is a need to provide a computer based system or method for processing large amounts of medical data quickly and efficiently to identify complex relationships between a particular drug or treatment regimen and the effects experienced by the user. The need is clear for collaborative and integrative approaches and strategies to allow faster identification of high-risk interactions between marketed drugs and adverse events, and to enable the automated uncovering of scientific evidence behind them.